Realistic Simulation of Underwater Scene for Image Enhancement

In recent years, learning-based methods have performed remarkably well in underwater image enhancement, but their performance is limited by the lack of high-quality, diverse training datasets. Current underwater image datasets are unable to address the following three issues: intra-domain gaps in underwater environments, inter-domain gaps between synthetic and real data, and domain inaccuracies. To overcome these limitations, we construct a realistic underwater scene using 3D graphics engine through a three-step approach: 1) integrate a simulation-specific underwater light propagation models to create volumetric fog; 2) employ physical model-based rendering for accurate light field simulation; 3) configure scenes with parameters extracted from real underwater images. Based on this framework, we develop an underwater image enhancement dataset (MUSE). Experiments demonstrate that models trained on MUSE outperform those trained on conventional datasets, highlighting the effectiveness of our approach.